Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The development of aperiodic neural activity in the human brain

Nature Human Behaviour (2025)Cite this article

Subjects

Abstract

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study (https://osf.io/gsru7), we analysed intracranial electroencephalography recordings from widespread brain regions in a large developmental cohort. Using task-based (that is, attention to to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93–54.00 years, 63 males; n electrodes = 5,691), we mapped aperiodic (1/ƒ-like) activity, a proxy of neural noise, where steeper slopes indicate less noise and flatter slopes indicate more noise. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In the prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in grey matter volume are similarly associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC control during adolescence in the development of attention and memory.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Design, channel coverage and key variables.
Fig. 2: Regional differences in the aperiodic slope and correlations with GMV.
Fig. 3: Age-related differences in aperiodic slopes between association and sensorimotor cortices.
Fig. 4: Regions with a significant interaction between age and attentional state on aperiodic activity.
Fig. 5: Task-based aperiodic slopes in the MFG predict age-related differences in memory performance.
Fig. 6: Regions with a significant effect of age on GMV.
Fig. 7: Regions with a significant interaction between age and GMV on the aperiodic slope.
Fig. 8: Aperiodic activity stabilizes in young adulthood, differs by age and attentional state, predicts age-related variability in episodic memory and is associated with age-related variability in GMV.

Similar content being viewed by others

Data availability

All data and code are available at https://tinyurl.com/m5yfc9ny.

References

  1. Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl Acad. Sci. USA 101, 8174–8179 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Grydeland, H. et al. Waves of maturation and senescence in micro-structural MRI markers of human cortical myelination over the lifespan. Cereb. Cortex 29, 1369–1381 (2019).

    Article  PubMed  Google Scholar 

  4. Favaro, J. et al. The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep. NeuroImage 277, 120264 (2023).

    Article  PubMed  Google Scholar 

  5. Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A. G. & Enticott, P. G. Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Dev. Cogn. Neurosci. 54, 101076 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Schaworonkow, N. & Voytek, B. Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life. Dev. Cogn. Neurosci. 47, 100895 (2021).

    Article  PubMed  Google Scholar 

  7. Tröndle, M., Popov, T., Dziemian, S. & Langer, N. Decomposing the role of alpha oscillations during brain maturation. eLife 11, e77571 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Donoghue, T. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23, 1655–1665 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wen, H. & Liu, Z. Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain Topogr. 29, 13–26 (2016).

    Article  PubMed  Google Scholar 

  10. Ahmad, J. et al. From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans. Transl. Psychiatry 12, 467 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  11. van Nifterick, A. M. et al. Resting-state oscillations reveal disturbed excitation–inhibition ratio in Alzheimer’s disease patients. Sci. Rep. 13, 7419 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Manning, J. R., Jacobs, J., Fried, I. & Kahana, M. J. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J. Neurosci. 29, 13613–13620 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Miller, K. J. et al. Human motor cortical activity is selectively phase-entrained on underlying rhythms. PLoS Comput. Biol. 9, e1002655 (2012).

    Article  Google Scholar 

  14. Kramer, M. A. & Chu, C. J. A general, noise-driven mechanism for the 1/f-like behavior of neural field spectra. Neural Comput. 36, 1643–1668 (2024).

    Article  PubMed  Google Scholar 

  15. Voytek, B. & Knight, R. T. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol. Psychiatry 77, 1089–1097 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Voytek, B. et al. Age-related changes in 1/f neural electrophysiological noise. J. Neurosci. 35, 13257–13265 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Bédard, C. & Destexhe, A. Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. Biophys. J. 96, 2589–2603 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Evertz, R., Hicks, D. G. & Liley, D. T. Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes. PLoS Comput. Biol. 18, e1010012 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Martínez‐Cañada, P. et al. Combining aperiodic 1/f slopes and brain simulation: an EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer’s disease. Alzheimers Dement. Diagn. Assess. Dis. Monit. 15, e12477 (2023).

    Google Scholar 

  21. Wiest, C. et al. The aperiodic exponent of subthalamic field potentials reflects excitation/inhibition balance in Parkinsonism. eLife 12, e82467 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci. 5, 97–107 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Earl, R. J., Ford, T. C., Lum, J. A. G., Enticott, P. G. & Hill, A. T. Exploring aperiodic activity in first episode schizophrenia spectrum psychosis: a resting-state EEG analysis. Brain Res. 1840, 149052 (2024).

    Article  CAS  PubMed  Google Scholar 

  24. Pani, S. M., Saba, L. & Fraschini, M. Clinical applications of EEG power spectra aperiodic component analysis: a mini-review. Clin. Neurophysiol. 143, 1–13 (2022).

    Article  PubMed  Google Scholar 

  25. Shuffrey, L. C. et al. Aperiodic electrophysiological activity in preterm infants is linked to subsequent autism risk. Dev. Psychobiol. 64, e22271 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fernandez, F. & Garner, C. C. Over-inhibition: a model for developmental intellectual disability. Trends Neurosci. 30, 497–503 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Merkin, A. et al. Do age-related differences in aperiodic neural activity explain differences in resting EEG alpha? Neurobiol. Aging 121, 78–87 (2023).

    Article  PubMed  Google Scholar 

  28. Waschke, L., Wöstmann, M. & Obleser, J. States and traits of neural irregularity in the age-varying human brain. Sci. Rep. 7, 17381 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Tran, T. T., Rolle, C. E., Gazzaley, A. & Voytek, B. Linked sources of neural noise contribute to age-related cognitive decline. J. Cogn. Neurosci. 32, 1813–1822 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Cross, Z. R., Corcoran, A. W., Schlesewsky, M., Kohler, M. J. & Bornkessel-Schlesewsky, I. Oscillatory and aperiodic neural activity jointly predict language learning. J. Cogn. Neurosci. 34, 1630–1649 (2022).

    Article  PubMed  Google Scholar 

  31. Lendner, J. D. et al. Oscillatory and aperiodic neuronal activity in working memory following anesthesia. Clin. Neurophysiol. 150, 79–88 (2023).

    Article  PubMed  Google Scholar 

  32. Cellier, D., Riddle, J., Petersen, I. & Hwang, K. The development of theta and alpha neural oscillations from ages 3 to 24 years. Dev. Cogn. Neurosci. 50, 100969 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ofen, N., Tang, L., Yu, Q. & Johnson, E. L. Memory and the developing brain: from description to explanation with innovation in methods. Dev. Cogn. Neurosci. 36, 100613 (2019).

    Article  PubMed  Google Scholar 

  34. Musall, S., von Pföstl, V., Rauch, A., Logothetis, N. K. & Whittingstall, K. Effects of neural synchrony on surface EEG. Cereb. Cortex 24, 1045–1053 (2014).

    Article  PubMed  Google Scholar 

  35. Palva, J. M. et al. Ghost interactions in MEG/EEG source space: a note of caution on inter-areal coupling measures. NeuroImage 173, 632–643 (2018).

    Article  PubMed  Google Scholar 

  36. Johnson, E. L., Kam, J. W., Tzovara, A. & Knight, R. T. Insights into human cognition from intracranial EEG: a review of audition, memory, internal cognition, and causality. J. Neural Eng. 17, 051001 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Johnson, E. L. & Knight, R. T. Intracranial recordings and human memory. Curr. Opin. Neurobiol. 31, 18–25 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Parvizi, J. & Kastner, S. Promises and limitations of human intracranial electroencephalography. Nat. Neurosci. 21, 474–483 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Johnson, E. L., Tang, L., Yin, Q., Asano, E. & Ofen, N. Direct brain recordings reveal prefrontal cortex dynamics of memory development. Sci. Adv. 4, eaat3702 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Johnson, E. L. et al. Dissociable oscillatory theta signatures of memory formation in the developing brain. Curr. Biol. 32, 1457–1469.e4 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Johnson, E. L. & Knight, R. T. How can iEEG be used to study inter-individual and developmental differences? in Intracranial EEG: A Guide for Cognitive Neuroscientists (ed Axmacher, N.) 143–154 (Springer, 2023).

  42. Rau, E. M. B. et al. Reinstatement and transformation of memory traces for recognition. Sci. Adv. 11, eadp9336 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Yin, Q. et al. Direct brain recordings reveal occipital cortex involvement in memory development. Neuropsychologia 148, 107625 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yin, Q., Johnson, E. L. & Ofen, N. Neurophysiological mechanisms of cognition in the developing brain: Insights from intracranial EEG studies. Dev. Cogn. Neurosci. 64, 101312 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Leszczyński, M. et al. Dissociation of broadband high-frequency activity and neuronal firing in the neocortex. Sci. Adv. 6, eabb0977 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Nir, Y. et al. Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations. Curr. Biol. 17, 1275–1285 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Ray, S., Crone, N. E., Niebur, E., Franaszczuk, P. J. & Hsiao, S. S. Neural correlates of high-gamma oscillations (60–200 Hz) in macaque local field potentials and their potential implications in electrocorticography. J. Neurosci. 28, 11526–11536 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Rich, E. L. & Wallis, J. D. Spatiotemporal dynamics of information encoding revealed in orbitofrontal high-gamma. Nat. Commun. 8, 1139 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Watson, B. O., Ding, M. & Buzsáki, G. Temporal coupling of field potentials and action potentials in the neocortex. Eur. J. Neurosci. 48, 2482–2497 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hunt, B. A. et al. Relationships between cortical myeloarchitecture and electrophysiological networks. Proc. Natl Acad. Sci. USA 113, 13510–13515 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Schölvinck, M. L., Leopold, D. A., Brookes, M. J. & Khader, P. H. The contribution of electrophysiology to functional connectivity mapping. NeuroImage 80, 297–306 (2013).

    Article  PubMed  Google Scholar 

  52. Doval, S. et al. When maturation is not linear: brain oscillatory activity in the process of aging as measured by electrophysiology. Brain Topogr. https://doi.org/10.1007/s10548-024-01064-0 (2024).

    Article  PubMed  Google Scholar 

  53. Overbye, K., Huster, R. J., Walhovd, K. B., Fjell, A. M. & Tamnes, C. K. Development of the P300 from childhood to adulthood: a multimodal EEG and MRI study. Brain Struct. Funct. 223, 4337–4349 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Sui, J., Huster, R., Yu, Q., Segall, J. & Calhoun, V. Function–structure associations of the brain: evidence from multimodal connectivity and covariance studies. NeuroImage 102, 11–23 (2014).

    Article  PubMed  Google Scholar 

  55. Whitford, T. J. et al. Brain maturation in adolescence: concurrent changes in neuroanatomy and neurophysiology. Hum. Brain Mapp. 28, 228–237 (2007).

    Article  PubMed  Google Scholar 

  56. Groeschel, S., Vollmer, B., King, M. & Connelly, A. Developmental changes in cerebral grey and white matter volume from infancy to adulthood. Int. J. Dev. Neurosci. 28, 481–489 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Finley, A. J., Angus, D. J., Van Reekum, C. M., Davidson, R. J. & Schaefer, S. M. Periodic and aperiodic contributions to theta‐beta ratios across adulthood. Psychophysiology 59, e14113 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Thuwal, K., Banerjee, A. & Roy, D. Aperiodic and periodic components of ongoing oscillatory brain dynamics link distinct functional aspects of cognition across adult lifespan. eNeuro https://doi.org/10.1523/eneuro.0224-21.2021 (2021).

  59. Hill, J. et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA 107, 13135–13140 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Ofen, N. et al. Development of the declarative memory system in the human brain. Nat. Neurosci. 10, 1198–1205 (2007).

    Article  CAS  PubMed  Google Scholar 

  62. Wilke, M., Krägeloh-Mann, I. & Holland, S. K. Global and local development of gray and white matter volume in normal children and adolescents. Exp. Brain Res. 178, 296–307 (2007).

    Article  PubMed  Google Scholar 

  63. Hill, P. F., King, D. R., Lega, B. C. & Rugg, M. D. Comparison of fMRI correlates of successful episodic memory encoding in temporal lobe epilepsy patients and healthy controls. NeuroImage 207, 116397 (2020).

    Article  PubMed  Google Scholar 

  64. Klein, A. & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, (2012).

  65. Fotiadis, P. et al. Myelination and excitation–inhibition balance synergistically shape structure-function coupling across the human cortex. Nat. Commun. 14, 6115 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Mahjoory, K., Schoffelen, J.-M., Keitel, A. & Gross, J. The frequency gradient of human resting-state brain oscillations follows cortical hierarchies. eLife 9, e53715 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Keller, A. S. et al. Hierarchical functional system development supports executive function. Trends Cogn. Sci. 27, 160–174 (2023).

    Article  PubMed  Google Scholar 

  68. Larsen, B., Sydnor, V. J., Keller, A. S., Yeo, B. T. T. & Satterthwaite, T. D. A critical period plasticity framework for the sensorimotor–association axis of cortical neurodevelopment. Trends Neurosci. 46, 847–862 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Sydnor, V. J. et al. Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth. Nat. Neurosci. 26, 638–649 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Tervo-Clemmens, B. et al. A canonical trajectory of executive function maturation from adolescence to adulthood. Nat. Commun. 14, 6922 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Momi, D. et al. Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks. Nat. Commun. 16, 3222 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Bornkessel-Schlesewsky, I. et al. Effects of neural noise on predictive model updating across the adult lifespan. Preprint at bioRxiv https://doi.org/10.1101/2022.12.14.520501 (2022).

  73. McSweeney, M. et al. Age-related trends in aperiodic EEG activity and alpha oscillations during early- to middle-childhood. NeuroImage 269, 119925 (2023).

    Article  PubMed  Google Scholar 

  74. Ouyang, G., Hildebrandt, A., Schmitz, F. & Herrmann, C. S. Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed. NeuroImage 205, 116304 (2020).

    Article  PubMed  Google Scholar 

  75. Gazit, T. et al. The role of mPFC and MTL neurons in human choice under goal-conflict. Nat. Commun. 11, 3192 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article  CAS  PubMed  Google Scholar 

  77. Noudoost, B. & Moore, T. Control of visual cortical signals by prefrontal dopamine. Nature 474, 372–375 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Dave, S., Brothers, T. A. & Swaab, T. Y. 1/f neural noise and electrophysiological indices of contextual prediction in aging. Brain Res. 1691, 34–43 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Sheehan, T. C., Sreekumar, V., Inati, S. K. & Zaghloul, K. A. Signal complexity of human intracranial EEG tracks successful associative-memory formation across individuals. J. Neurosci. 38, 1744 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Immink, M. A. et al. Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning. Hum. Mov. Sci. 78, 102829 (2021).

    Article  PubMed  Google Scholar 

  81. Dziego, C. A. et al. Neural and cognitive correlates of performance in dynamic multi-modal settings. Neuropsychologia 180, 108483 (2023).

    Article  PubMed  Google Scholar 

  82. Fuster, J. M. Frontal lobe and cognitive development. J. Neurocytol. 31, 373–385 (2002).

    Article  PubMed  Google Scholar 

  83. Ridderinkhof, K. R., Ullsperger, M., Crone, E. A. & Nieuwenhuis, S. The role of the medial frontal cortex in cognitive control. Science 306, 443–447 (2004).

    Article  CAS  PubMed  Google Scholar 

  84. Robertson, M. M. et al. EEG power spectral slope differs by ADHD status and stimulant medication exposure in early childhood. J. Neurophysiol. 122, 2427–2437 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Molina, J. L. et al. Memantine effects on electroencephalographic measures of putative excitatory/inhibitory balance in schizophrenia. Biol. Psychiatry Cogn. Neurosci. Neuroimag. 5, 562–568 (2020).

    Google Scholar 

  86. Peterson, E. J., Rosen, B. Q., Belger, A., Voytek, B. & Campbell, A. M. Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations. Clin. EEG Neurosci. 54, 434–445 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Kolk, S. M. & Rakic, P. Development of prefrontal cortex. Neuropsychopharmacology 47, 41–57 (2022).

    Article  CAS  PubMed  Google Scholar 

  88. McKeon, S. D. et al. Aperiodic EEG and 7T MRSI evidence for maturation of E/I balance supporting the development of working memory through adolescence. Dev. Cogn. Neurosci. 66, 101373 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Sukenik, N. et al. Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers. Proc. Natl Acad. Sci. USA 118, e2018459118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Bornkessel-Schlesewsky, I. et al. Rapid adaptation of predictive models during language comprehension: aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating. Front. Psychol. 13, 817516 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Braver, T. S., Paxton, J. L., Locke, H. S. & Barch, D. M. Flexible neural mechanisms of cognitive control within human prefrontal cortex. Proc. Natl Acad. Sci. USA 106, 7351–7356 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Cabeza, R. et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 19, 701–710 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Spreng, R. N. & Turner, G. R. The shifting architecture of cognition and brain function in older adulthood. Perspect. Psychol. Sci. 14, 523–542 (2019).

    Article  PubMed  Google Scholar 

  94. Buzsaki, G. Rhythms of the Brain (Oxford Univ. Press, 2006).

  95. Nunez, P. L. & Srinivasan, R. Recording strategies, reference issues, and dipole localization. Nunez PL Srinivasan R. Electr. Fields Brain Neurophys. EEG Ed. 2, 275–312 (2006).

    Article  Google Scholar 

  96. Euler, M. J. et al. Associations between the resting EEG aperiodic slope and broad domains of cognitive ability. Psychophysiology 61, e14543 (2024).

    Article  PubMed  Google Scholar 

  97. Montemurro, S. et al. Aperiodic component of EEG power spectrum and cognitive performance are modulated by education in aging. Sci. Rep. 14, 15111 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Pi, Y. et al. Interindividual aperiodic resting-state EEG activity predicts cognitive-control styles. Psychophysiology 61, e14576 (2024).

    Article  PubMed  Google Scholar 

  99. Larsen, B. et al. A developmental reduction of the excitation:inhibition ratio in association cortex during adolescence. Sci. Adv. 8, eabj8750 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Miller, K. J., Sorensen, L. B., Ojemann, J. G. & den Nijs, M. Power-law scaling in the brain surface electric potential. PLoS Comput. Biol. 5, e1000609 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Herweg, N. A., Solomon, E. A. & Kahana, M. J. Theta oscillations in human memory. Trends Cogn. Sci. 24, 208–227 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Lega, B. C., Jacobs, J. & Kahana, M. Human hippocampal theta oscillations and the formation of episodic memories. Hippocampus 22, 748–761 (2012).

    Article  PubMed  Google Scholar 

  103. Josefsson, M. et al. Memory profiles predict dementia over 23–28 years in normal but not successful aging. Int. Psychogeriatr. 35, 351–359 (2023).

    Article  PubMed  Google Scholar 

  104. Riley, K. P. et al. Prediction of preclinical Alzheimer’s disease: longitudinal rates of change in cognition. J. Alzheimer’s Dis. 25, 707–717 (2011).

    Article  Google Scholar 

  105. Chai, X. J., Ofen, N., Jacobs, L. F. & Gabrieli, J. D. Scene complexity: influence on perception, memory, and development in the medial temporal lobe. Front. Hum. Neurosci. 4, 1021 (2010).

    Article  Google Scholar 

  106. Ofen, N., Chai, X. J., Schuil, K. D., Whitfield-Gabrieli, S. & Gabrieli, J. D. The development of brain systems associated with successful memory retrieval of scenes. J. Neurosci. 32, 10012–10020 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Tang, L., Shafer, A. T. & Ofen, N. Prefrontal cortex contributions to the development of memory formation. Cereb. Cortex 28, 3295–3308 (2018).

    Article  PubMed  Google Scholar 

  108. Davoudi, S., Parto Dezfouli, M., Knight, R. T., Daliri, M. R. & Johnson, E. L. Prefrontal lesions disrupt posterior alpha–gamma coordination of visual working memory representations. J. Cogn. Neurosci. 33, 1798–1810 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Dezfouli, M. P., Davoudi, S., Knight, R. T., Daliri, M. R. & Johnson, E. L. Prefrontal lesions disrupt oscillatory signatures of spatiotemporal integration in working memory. Cortex 138, 113–126 (2021).

    Article  PubMed Central  Google Scholar 

  110. Johnson, E. L. et al. Dynamic frontotemporal systems process space and time in working memory. PLoS Biol. 16, e2004274 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Johnson, E. L. et al. Orbitofrontal cortex governs working memory for temporal order. Curr. Biol. 32, R410–R411 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Johnson, E. L. et al. Bidirectional frontoparietal oscillatory systems support working memory. Curr. Biol. 27, 1829–1835 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Johnson, E. L. et al. Spectral imprints of working memory for everyday associations in the frontoparietal network. Front. Syst. Neurosci. 12, 65 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Mercier, M. R. et al. Advances in human intracranial electroencephalography research, guidelines and good practices. NeuroImage 260, 119438 (2022).

    Article  PubMed  Google Scholar 

  115. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 1–9 (2011).

    Article  Google Scholar 

  116. Rossini, L. et al. Seizure activity per se does not induce tissue damage markers in human neocortical focal epilepsy. Ann. Neurol. 82, 331–341 (2017).

    Article  CAS  PubMed  Google Scholar 

  117. Vallat, R. & Walker, M. P. An open-source, high-performance tool for automated sleep staging. Elife 10, e70092 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Stolk, A. et al. Integrated analysis of anatomical and electrophysiological human intracranial data. Nat. Protoc. 13, 1699–1723 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).

    Article  PubMed  Google Scholar 

  120. Groppe, D. M. et al. iELVis: An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. J. Neurosci. Methods 281, 40–48 (2017).

    Article  CAS  PubMed  Google Scholar 

  121. Adamek, M., Swift, J. R. & Brunner, P. VERA-Versatile electrode localization Framework. Zenodo https://doi.org/10.5281/zenodo.7486841 (2022).

  122. Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Article  Google Scholar 

  123. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Article  Google Scholar 

  124. Fox, J. et al. The car package. R. Found. Stat. Comput. 1109, 1431 (2007).

    Google Scholar 

  125. Lüdecke, D. ggeffects: Tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).

    Article  Google Scholar 

  126. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).

  127. Brehm, L. & Alday, P. M. Contrast coding choices in a decade of mixed models. J. Mem. Lang. 125, 104334 (2022).

    Article  Google Scholar 

  128. Austin, P. C. & Hux, J. E. A brief note on overlapping confidence intervals. J. Vasc. Surg. 36, 194–195 (2002).

    Article  PubMed  Google Scholar 

  129. MacGregor-Fors, I. & Payton, M. E. Contrasting diversity values: statistical inferences based on overlapping confidence intervals. PLoS ONE 8, e56794 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Tukey, J. W. Exploratory Data Analysis Vol. 2 131–160 (Addison-Wesley, 1977).

  131. Mowinckel, A. M. & Vidal-Piñeiro, D. Visualization of brain statistics with R packages ggseg and ggseg3d. Adv. Methods Pract. Psychol. Sci. 3, 466–483 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part through the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research and Northwestern University Information Technology. We thank P. M. Alday for helpful discussions regarding statistical modelling and K. I. Auguste for assistance with patient recruitment. Funding was provided by R00NS115918 (E.L.J.), R01MH107512 (N.O.), R01NS21135 (R.T.K.), R00MH117226, P30AG013854, DGE-2234667 (Y.M.R.), T32MH067564 (Y.M.R. and C.C.), T32NS047987 (A.M.H.) and P41EB018783. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Northwestern University, Chicago, IL, USA

    Zachariah R. Cross, Samantha M. Gray, Adam J. O. Dede, Yessenia M. Rivera, Christopher Cyr, Ania M. Holubecki, Stephan U. Schuele, Joshua M. Rosenow, Joyce Y. Wu, Sandi K. Lam, Jeffrey S. Raskin, Rodrigo M. Braga & Elizabeth L. Johnson

  2. Stanford University, Stanford, CA, USA

    Samantha M. Gray

  3. Wayne State University, Detroit, MI, USA

    Qin Yin, Parisa Vahidi, Eishi Asano & Noa Ofen

  4. University of Texas, Dallas, Dallas, TX, USA

    Qin Yin & Noa Ofen

  5. Ruhr University Bochum, Bochum, Germany

    Elias M. B. Rau

  6. University of California, Davis, Davis, CA, USA

    Jack J. Lin, Ignacio Saez & Fady Girgis

  7. University of California, San Diego, and Rady Children’s Hospital, San Diego, CA, USA

    Olivia Kim McManus & Shifteh Sattar

  8. Ichan School of Medicine at Mount Sinai, New York City, NY, USA

    Ignacio Saez

  9. University of Calgary, Calgary, Alberta, Canada

    Fady Girgis

  10. California Pacific Medical Center, San Francisco, CA, USA

    David King-Stephens, Peter B. Weber & Kenneth D. Laxer

  11. Yale University, New Haven, CT, USA

    David King-Stephens

  12. Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA

    Joyce Y. Wu, Sandi K. Lam & Jeffrey S. Raskin

  13. University of California, San Francisco, San Francisco, CA, USA

    Edward F. Chang

  14. Ohio State University and Nationwide Children’s Hospital, Columbus, OH, USA

    Ammar Shaikhouni

  15. Washington University in St. Louis, St. Louis, MO, USA

    Peter Brunner & Jarod L. Roland

  16. St. Louis Children’s Hospital, St. Louis, MO, USA

    Jarod L. Roland

  17. University of California, Berkeley, Berkeley, CA, USA

    Robert T. Knight

Authors
  1. Zachariah R. Cross
    View author publications

    Search author on:PubMed Google Scholar

  2. Samantha M. Gray
    View author publications

    Search author on:PubMed Google Scholar

  3. Adam J. O. Dede
    View author publications

    Search author on:PubMed Google Scholar

  4. Yessenia M. Rivera
    View author publications

    Search author on:PubMed Google Scholar

  5. Qin Yin
    View author publications

    Search author on:PubMed Google Scholar

  6. Parisa Vahidi
    View author publications

    Search author on:PubMed Google Scholar

  7. Elias M. B. Rau
    View author publications

    Search author on:PubMed Google Scholar

  8. Christopher Cyr
    View author publications

    Search author on:PubMed Google Scholar

  9. Ania M. Holubecki
    View author publications

    Search author on:PubMed Google Scholar

  10. Eishi Asano
    View author publications

    Search author on:PubMed Google Scholar

  11. Jack J. Lin
    View author publications

    Search author on:PubMed Google Scholar

  12. Olivia Kim McManus
    View author publications

    Search author on:PubMed Google Scholar

  13. Shifteh Sattar
    View author publications

    Search author on:PubMed Google Scholar

  14. Ignacio Saez
    View author publications

    Search author on:PubMed Google Scholar

  15. Fady Girgis
    View author publications

    Search author on:PubMed Google Scholar

  16. David King-Stephens
    View author publications

    Search author on:PubMed Google Scholar

  17. Peter B. Weber
    View author publications

    Search author on:PubMed Google Scholar

  18. Kenneth D. Laxer
    View author publications

    Search author on:PubMed Google Scholar

  19. Stephan U. Schuele
    View author publications

    Search author on:PubMed Google Scholar

  20. Joshua M. Rosenow
    View author publications

    Search author on:PubMed Google Scholar

  21. Joyce Y. Wu
    View author publications

    Search author on:PubMed Google Scholar

  22. Sandi K. Lam
    View author publications

    Search author on:PubMed Google Scholar

  23. Jeffrey S. Raskin
    View author publications

    Search author on:PubMed Google Scholar

  24. Edward F. Chang
    View author publications

    Search author on:PubMed Google Scholar

  25. Ammar Shaikhouni
    View author publications

    Search author on:PubMed Google Scholar

  26. Peter Brunner
    View author publications

    Search author on:PubMed Google Scholar

  27. Jarod L. Roland
    View author publications

    Search author on:PubMed Google Scholar

  28. Rodrigo M. Braga
    View author publications

    Search author on:PubMed Google Scholar

  29. Robert T. Knight
    View author publications

    Search author on:PubMed Google Scholar

  30. Noa Ofen
    View author publications

    Search author on:PubMed Google Scholar

  31. Elizabeth L. Johnson
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Z.R.C., N.O. and E.L.J. designed the study. S.M.G., A.J.O.D., Y.M.R., C.R., A.M.H., E.A., J.J.L., O.K.M., S.S., I.S., F.G., D.K.-S., P.B.W., K.D.L., S.U.S., J.M.R., J.Y.W., S.K.L., J.S.R., E.F.C., A.S., P.B., J.L.R., R.M.B. and E.L.J. recruited patients and/or collected data. Z.R.C., S.M.G., Q.Y., P.V., E.M.B.R., C.C., A.M.H., R.T.K., N.O. and E.L.J. preprocessed data. Z.R.C. and A.J.O.D. analysed data. Z.R.C. visualized results. Z.R.C. and E.L.J. interpreted data. Z.R.C. drafted the manuscript. Z.R.C. and E.L.J. revised the manuscript. E.L.J. supervised the study. All authors provided feedback on the completed manuscript.

Corresponding author

Correspondence to Zachariah R. Cross.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Aron Hill, Ezequiel Mikulan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15 and Tables 1–4.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cross, Z.R., Gray, S.M., Dede, A.J.O. et al. The development of aperiodic neural activity in the human brain. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02270-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41562-025-02270-x

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing